Examples of driver assistance systems in which example embodiments of the present invention are applicable include so-called predictive safety systems (PSS) and adaptive cruise control (ACC) systems. In both types of driver assistance systems, at least one object position-finding system, e.g., a radar system or a video-based system, is provided for monitoring the surroundings of the vehicle equipped with the assistance system and for locating objects in the surroundings of the vehicle, in particular preceding vehicles and other obstacles.
With a PSS system, a “predictive” calculation is performed on the basis of the position data to calculate whether there will be a collision with an object, and if there is an acute risk of a collision, a warning signal is issued. This warning signal is converted into an audible signal, for example, which should direct the driver's attention to the risk. Systems are also being developed with which the warning signal immediately triggers an automatic intervention into the longitudinal guidance of the vehicle, e.g., emergency braking, to prevent the collision or at least ameliorate its consequences.
However, such systems make very high demands on the accuracy and reliability of the position-finding system because otherwise there may often be faulty deployment, which could in turn constitute a considerable source of risk.
The reliability of the object position-finding may be improved by providing two position-finding systems that operate independently of one another, so that a certain redundancy is achieved.
DE 103 99 943 describes a method that operates using two independent position-finding systems, one of which supplies data optimized for the longitudinal value while the other system supplies data optimized for the lateral value or lateral extent.
One example of a system optimized for the longitudinal value is a radar system, which supplies relatively accurate measured values for the distance and relative speed of an object, but supplies only inaccurate data for the azimuth angle and thus the lateral position of the object even when the radar sensor has a certain angular resolution. Also, using such a sensor, the lateral extent of the object may be determined only very roughly.
One example of a system optimized for the lateral value is a video-based system, e.g., a video camera having the respective electronic image processing system. Such a system is capable of supplying relatively accurate data about the azimuth angle and the lateral extent of an object, but it allows only an inaccurate determination or estimate of the object distance, in particular with monocular systems, and the relative speed may be determined only indirectly by derivation of the inaccurate distance data over time. In the case of a monocular video system, the distance may be estimated only approximately on the basis of the height of the object in the video image in relation to the height of the horizontal line. If necessary, the accuracy may be improved somewhat by a road surface estimate. A binocular video system allows a distance determination by triangulation, but it also yields only relatively inaccurate values, in particular at greater distances.
In the aforementioned document, it is proposed that the position data of the two systems be compared with one another to obtain more accurate and more plausible position data about the objects located. This procedure is known as checking the plausibility of objects. When a presumed object has been located with the help of one of the two systems, it is possible to state with a high probability that it is a real object if the position data are confirmed by the other system.
Through fusion of the position data of the two position-finding systems, the particular weaknesses of these systems may also be compensated to a certain extent. For example, if the system that has been optimized for the longitudinal value locates an object whose lateral position and extent may be given only within relatively wide tolerance limits, then a check may be performed with the help of the system optimized for the lateral value to determine whether this system has located an object within the wide tolerance limits. The system optimized for the lateral value will then in turn be able to give the distance of the object only within relatively great error limits. If the distance measured by the system optimized for the longitudinal value is within these error limits, then the assumption that the objects located by the two systems are the same physical object is plausible and the exact distance and relative speed measured by the system optimized for the longitudinal value may be combined with the precise data about the precise lateral position and lateral extent measured by the system optimized for the lateral value.
However, there remains a bit of uncertainty with regard to the question of whether or not the position data supplied by the two independent systems actually describe the same real object. This is of course true in particular when there are two objects relatively close together or when there is a relatively high object density in general.
DE 10 2004 046 360 describes a PSS system in which the so-called time to collision is calculated in advance for determining the risk of collision, i.e., this is the time that will presumably elapse before collision with the object if there is no change in the dynamic data for the host vehicle and the located object. The PSS system then triggers one or more actions to prevent the collision or to ameliorate the consequences of the collision when the time to collision is below a threshold value provided for the particular action. It is also proposed in this publication that radar-based systems and image processing-based systems may be combined with one another for the object position-finding but it is not explained in greater detail how the data of these systems are to be combined.
WO 2005/098782 describes a method by which, in a video-based system, the time to collision may be calculated from the change in the scale factor of a detected object from one measuring cycle to the next without having to know the object distances exactly at the particular measuring times.